Extracellular vesicles (EVs) are naturally secreted nanoscale mediators of intercellular communication, showing potential for therapeutic and functional food applications. Although many EVs are being isolated with claims of therapeutic benefits, the evaluation criteria require extensive resources and time, often resulting in futile outcomes. This work addresses this gap by developing a visual and quantitative system using monk fruit cell-derived EVs (MFEVs) as a model to efficiently select the most suitable therapeutic EVs by analyzing their characterization parameters. This approach saves valuable resources and time. To generate variations, MFEVs were isolated using eight different techniques: ultracentrifugation, ultrafiltration, polyethylene glycol (PEG) precipitation (8 %, 10 %, 15 %, and 20 %), anion-exchange chromatography, and a novel combined ultrafiltration-precipitation method. Following isolation, their physicochemical properties, biochemical composition, and bioactivity were characterized, and their dose-dependent anticancer effects were evaluated across multiple cancer cell lines. Next, using data from the correlative statistics of anticancer activity with characterization parameters, “ExoOrb” is developed. It is an analytical multicriteria decision-making system that objectively ranks the therapeutic potential of EVs by employing factor normalization, weighted scoring, and multidimensional visualizations. The system has been validated using both the original dataset and synthetic datasets. The original dataset identified PEG 10 %-MFEVs as more effective therapeutically, and the synthetic dataset confirmed ExoOrb's ability for metrisizing EVs across multiple EVs types. To our knowledge, ExoOrb is the first potentially universal framework for evaluating the therapeutic potential of EVs based on characterization parameters, providing a reliable tool for scientific and therapeutic research through standardized, data-driven optimization.
ExoOrb: A novel visual and analytical system for therapeutic extracellular vesicles metrics
Rahman, Muhammad Rameez Ur;Vascon, Sebastiano;
2025-01-01
Abstract
Extracellular vesicles (EVs) are naturally secreted nanoscale mediators of intercellular communication, showing potential for therapeutic and functional food applications. Although many EVs are being isolated with claims of therapeutic benefits, the evaluation criteria require extensive resources and time, often resulting in futile outcomes. This work addresses this gap by developing a visual and quantitative system using monk fruit cell-derived EVs (MFEVs) as a model to efficiently select the most suitable therapeutic EVs by analyzing their characterization parameters. This approach saves valuable resources and time. To generate variations, MFEVs were isolated using eight different techniques: ultracentrifugation, ultrafiltration, polyethylene glycol (PEG) precipitation (8 %, 10 %, 15 %, and 20 %), anion-exchange chromatography, and a novel combined ultrafiltration-precipitation method. Following isolation, their physicochemical properties, biochemical composition, and bioactivity were characterized, and their dose-dependent anticancer effects were evaluated across multiple cancer cell lines. Next, using data from the correlative statistics of anticancer activity with characterization parameters, “ExoOrb” is developed. It is an analytical multicriteria decision-making system that objectively ranks the therapeutic potential of EVs by employing factor normalization, weighted scoring, and multidimensional visualizations. The system has been validated using both the original dataset and synthetic datasets. The original dataset identified PEG 10 %-MFEVs as more effective therapeutically, and the synthetic dataset confirmed ExoOrb's ability for metrisizing EVs across multiple EVs types. To our knowledge, ExoOrb is the first potentially universal framework for evaluating the therapeutic potential of EVs based on characterization parameters, providing a reliable tool for scientific and therapeutic research through standardized, data-driven optimization.
I documenti in ARCA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
